1. Field of the Invention
The present invention relates to a system and a method for index reorganization using a partial index transfer in a spatial data warehouse and, more particularly, to a system and a method for index reorganization using a partial index transfer in a spatial data warehouse that minimize costs of search, fragmentation and readjustment for the index reorganization by organizing and transferring clusters with partial indexes in conformity with an index structure so that the partial indexes may be inserted directly into the existing index, thus ensuring continuous and stable data processes and enhancing the capabilities of the system.
2. Description of Related Art
Data warehouse is a database that is extracted, converted, integrated and summarized via various operating systems to help users to make decisions. IBM introduced such concept initially in the mid-1980s for the purpose of marketing their hardwares using a term of information warehouse.
Data warehouse comprising a primitive data layer, a data warehouse layer and a client layer extracts, stores, inquires data. In such data warehouse, the operating system provides automated inventory control and accounting information management, required for organization management, and specialized functions such as a business system, and the data warehouse builds data with priority given to the themes such as clients, products, accounting, etc.
Spatial data warehouse for processing spatial data comprising a spatial data warehouse server and a builder storing all changes of source data to a repository thereof and loading the stored data into the spatial data warehouse server via a batch processing by regular periods.
Moreover, the data warehouse server interrupts the user service to load data transferred from the builder for the purpose of building indexes and builds indexes using the loaded data to provide prompt responses to user's queries.
Conventional methods for organizing indexes include a bulk insertion technique, a seeded clustering technique and an index transfer technique.
The bulk insertion technique of multidimensional indexes generates several clusters by clustering data spatially adjacent to one another. The respective clusters generated are composed of Small-Trees and the respective Small-Trees are inserted into the existing R-Tree.
Meanwhile, data, not included in any clusters, are classified as outliers and such data are inserted in ones via a well-known insertion algorithm. Such technique requires greater cost in clustering data and causes a greater overlap between the existing R-Tree nodes and newly inserted Small-Tree nodes. Accordingly, the bulk insertion technique has some drawbacks that deteriorate the performances of insertion and search (L. Chen, R. Choubey and E. A. Rundensteiner, “Bulk-insertions into R-tree using the small-tree-large-tree approach”, ACM GIS, 1998).
The seeded clustering technique clusters data in conformity with the existing index structure, not with the conventional spatial proximity. Since the clustering technique according to the spatial proximity does not consider the existing index structure, it has a strong possibility of expanding index areas and its complicated process requires lots of clustering costs.
Meanwhile, since the seeded clustering technique clusters data by inserting data into a root section of index, it causes a lesser overlap between nodes than the conventional clustering technique, which results in a rapid clustering process; however, it also has a strong possibility of expanding index areas and its complicated process requires lots of clustering costs (Taewon Lee, Bongki Moon and Sukho Lee “Bulk Insertion for R-Tree by Seeded Clustering”, DEXA, 2003).
The index transfer technique organizes indexes by directly transferring the index structure for reusing of the existing index. The problem of physical mapping of the existing index due to the index transfer is solved by reserving consecutive extents. According to this technique, it is possible to reduce the index organization cost by removing search, segmentation and minimum bounding rectangles (MBR) reorganization costs for the index organization via the index transfer, however, it is unsuitable for the index reorganization technique of the spatial data warehouse in which periodical source data changes occur (Sang-Keun Park, Ho-Seok Kim, Jae-Dong Lee and Hae-Young Bae, “An efficient index transfer method for reducing index organization cost in distributed database systems”, Korean Information Science Society, 2003).